Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model
1
School of Statistics, Renmin University of China, Beijing 100872, China
2
College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi 830011, China
3
Department of Mathematics, Statistics and Insurance, Hang Seng University of Hong Kong, Hong Kong, China
4
Department of Supply Chain and Information Management, Hang Seng University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(2), 774; https://doi.org/10.3390/ijerph18020774
Received: 16 December 2020
/
Revised: 10 January 2021
/
Accepted: 13 January 2021
/
Published: 18 January 2021
(This article belongs to the Section Public Health Statistics and Risk Assessment)
With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.
View Full-Text
Keywords:
columnar density of total atmospheric ozone; COVID-19; maximum temperature; minimum temperature; spatio-temporal multivariate time-series analysis; USA
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Rui, R.; Tian, M.; Tang, M.-L.; Ho, G.T.-S.; Wu, C.-H. Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model. Int. J. Environ. Res. Public Health 2021, 18, 774. https://doi.org/10.3390/ijerph18020774
AMA Style
Rui R, Tian M, Tang M-L, Ho GT-S, Wu C-H. Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model. International Journal of Environmental Research and Public Health. 2021; 18(2):774. https://doi.org/10.3390/ijerph18020774
Chicago/Turabian StyleRui, Rongxiang, Maozai Tian, Man-Lai Tang, George T.-S. Ho, and Chun-Ho Wu. 2021. "Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model" International Journal of Environmental Research and Public Health 18, no. 2: 774. https://doi.org/10.3390/ijerph18020774
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
